论文标题
具有离散和连续的超级款特征的基于贪婪的过渡依赖性解析
Greedy Transition-Based Dependency Parsing with Discrete and Continuous Supertag Features
论文作者
论文摘要
我们研究了丰富的超级列表特征在基于贪婪转变的依赖解析中的影响。虽然先前的研究表明，稀疏的布尔特征代表一个单词的1好的超级词可以提高解析精度，但我们表明，通过添加一个单词的整个Supertag分布的连续矢量表示，我们可以获得进一步的改进。通过这种方式，我们将获得基于贪婪的过渡的解析，并带有超级款项功能，其$ 88.6 \％$ $ las和$ 90.9 \％$ $ uason英国宾夕法尼亚州宾夕法尼亚州宾夕法尼亚州宾夕法尼亚州转换为斯坦福大学的依赖。
We study the effect of rich supertag features in greedy transition-based dependency parsing. While previous studies have shown that sparse boolean features representing the 1-best supertag of a word can improve parsing accuracy, we show that we can get further improvements by adding a continuous vector representation of the entire supertag distribution for a word. In this way, we achieve the best results for greedy transition-based parsing with supertag features with $88.6\%$ LAS and $90.9\%$ UASon the English Penn Treebank converted to Stanford Dependencies.
